Weekly Exercises #4

library(tidyverse)     

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## ✓ readr   2.0.1     ✓ forcats 0.5.1

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library(lubridate)     

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library(openintro)    

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library(palmerpenguins)
library(maps)          

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library(ggmap)         

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library(gplots)       

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library(RColorBrewer) 
library(sf)            

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library(leaflet)       
library(carData)      

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library(ggthemes)     
theme_set(theme_minimal())

# Starbucks locations
Starbucks <- read_csv("https://www.macalester.edu/~ajohns24/Data/Starbucks.csv")

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## chr (11): Brand, Store Number, Store Name, Ownership Type, Street Address, C...
## dbl  (2): Longitude, Latitude

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starbucks_us_by_state <- Starbucks %>% 
  filter(Country == "US") %>% 
  count(`State/Province`) %>% 
  mutate(state_name = str_to_lower(abbr2state(`State/Province`))) 

# Lisa's favorite St. Paul places - example for you to create your own data
favorite_stp_by_lisa <- tibble(
  place = c("Home", "Macalester College", "Adams Spanish Immersion", 
            "Spirit Gymnastics", "Bama & Bapa", "Now Bikes",
            "Dance Spectrum", "Pizza Luce", "Brunson's"),
  long = c(-93.1405743, -93.1712321, -93.1451796, 
           -93.1650563, -93.1542883, -93.1696608, 
           -93.1393172, -93.1524256, -93.0753863),
  lat = c(44.950576, 44.9378965, 44.9237914,
          44.9654609, 44.9295072, 44.9436813, 
          44.9399922, 44.9468848, 44.9700727)
  )

#COVID-19 data from the New York Times
covid19 <- read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv")

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## dbl  (2): cases, deaths
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Put your homework on GitHub!

Instructions

• Put your name at the top of the document.

• For ALL graphs, you should include appropriate labels.

• Feel free to change the default theme, which I currently have set to theme_minimal().

• Use good coding practice. Read the short sections on good code with pipes and ggplot2. This is part of your grade!

• When you are finished with ALL the exercises, uncomment the options at the top so your document looks nicer. Don’t do it before then, or else you might miss some important warnings and messages.

Warm-up exercises from tutorial

These exercises will reiterate what you learned in the “Mapping data with R” tutorial. If you haven’t gone through the tutorial yet, you should do that first.

Starbucks locations (ggmap)

1. Add the Starbucks locations to a world map. Add an aesthetic to the world map that sets the color of the points according to the ownership type. What, if anything, can you deduce from this visualization?

world <- get_stamenmap(
  bbox = c(left = -180, bottom = -57, right = 204, top = 84),
  maptype = "terrain",
  zoom = 2
)

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ggmap(world) +
  geom_point(data = Starbucks, 
            aes(x = Longitude, y = Latitude, color = `Ownership Type`),
            size = .1, 
            alpha = 0.5) +
  scale_color_viridis_d() +
  labs(title = "Starbucks Location") +
  theme_map() +
  theme(legend.background = element_blank(),
        legend.justification = "center",
        legend.position = "bottom",
        legend.direction = "horizontal",
        plot.title = element_text(hjust = 0.5, face = "bold", size = 14))

## Warning: Removed 1 rows containing missing values (geom_point).

Most Starbucks in Japan is joint venture, and Starbucks in China are mainly company owned or franchise. Starbucks in North America is not joint venture. Most Starbucks in Europe are on western part. There is no Starbucks in Africa, central and northern Asia, and few in Australia and South America.

2. Construct a new map of Starbucks locations in the Twin Cities metro area (approximately the 5 county metro area).

TwinCities <- get_stamenmap(
  bbox = c(left = -93.5806, bottom = 44.7887, right = -92.8321, top = 45.1249),
  maptype = "toner",
  zoom = 11
)

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ggmap(TwinCities) +
  geom_point(data = Starbucks, 
             aes(x = Longitude, y = Latitude),
             size = .8,
             color = "firebrick")+
  theme_map() +
  theme(legend.background = element_blank()) 

## Warning: Removed 25486 rows containing missing values (geom_point).

3. In the Twin Cities plot, play with the zoom number. What does it do? (just describe what it does - don’t actually include more than one map).

The zoom number control the size and clarity of the graph. If the zoom number is larger, the map will include more details, and the area include in the map will be larger. Larger the zoom number, longer it takes to run.

4. Try a couple different map types (see get_stamenmap() in help and look at maptype). Include a map with one of the other map types.

TwinCities <- get_stamenmap(
  bbox = c(left = -93.5806, bottom = 44.7887, right = -92.8321, top = 45.1249),
  maptype = "toner",
  zoom = 11
)
ggmap(TwinCities) +
  geom_point(data = Starbucks, 
             aes(x = Longitude, y = Latitude),
             size =.8,
             color = "firebrick") +
  theme_map() +
  theme(legend.background = element_blank()) 

## Warning: Removed 25486 rows containing missing values (geom_point).

5. Add a point to the map that indicates Macalester College and label it appropriately. There are many ways you can do think, but I think it’s easiest with the annotate() function (see ggplot2 cheatsheet).

ggmap(TwinCities) +
  geom_point(data = Starbucks, 
             aes(x = Longitude, y = Latitude),
             size = .8,
             color = "firebrick") +
  annotate(geom = "point",
           x = -93.1810,
           y = 44.9431,
           color = "darkolivegreen4") +
  annotate(geom = "text",
           x = -93.1810,
           y = 44.9325,
           color = "darkolivegreen4",
           label = "MAC") +
  theme_map() +
  theme(legend.background = element_blank()) 

## Warning: Removed 25486 rows containing missing values (geom_point).

Choropleth maps with Starbucks data (geom_map())

The example I showed in the tutorial did not account for population of each state in the map. In the code below, a new variable is created, starbucks_per_10000, that gives the number of Starbucks per 10,000 people. It is in the starbucks_with_2018_pop_est dataset.

census_pop_est_2018 <- read_csv("https://www.dropbox.com/s/6txwv3b4ng7pepe/us_census_2018_state_pop_est.csv?dl=1") %>% 
  separate(state, into = c("dot","state"), extra = "merge") %>% 
  select(-dot) %>% 
  mutate(state = str_to_lower(state))

## Rows: 51 Columns: 2

## ─ Column specification ────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (1): est_pop_2018

## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

starbucks_with_2018_pop_est <-
  starbucks_us_by_state %>% 
  left_join(census_pop_est_2018,
            by = c("state_name" = "state")) %>% 
  mutate(starbucks_per_10000 = (n/est_pop_2018)*10000)

6. dplyr review: Look through the code above and describe what each line of code does.

read_csv: read the .csv file.

separate: separate state name into two columns, dot and state. There is no values before the dots, so dot column will be empty. State column will left only the state name. “extra =”merge"" makes the column only splits at most length, which make the state name that has more than one word into one column.

select: remove the dots

mutate: create and add a new “state” to replace the old “state” in the table. “str_to_lower” changes the state variable to all lowercase.

left_join: starbucks_us_by_state data and join to it the data in census_pop_est_2018 by state_name in starbucks_us_by_state and state in census_pop_est_2018, which state_name and state means the same.

mutate: creates and adds a new variable in the table that computes the number of Starbucks per 10,000 people

7. Create a choropleth map that shows the number of Starbucks per 10,000 people on a map of the US. Use a new fill color, add points for all Starbucks in the US (except Hawaii and Alaska), add an informative title for the plot, and include a caption that says who created the plot (you!). Make a conclusion about what you observe.

state_map <- map_data("state")
starbucks_with_2018_pop_est %>% 
  ggplot(aes()) +
  geom_map(map = state_map, 
           aes(map_id = state_name,
               fill = starbucks_per_10000)) +
  geom_point(data = Starbucks %>% 
               filter(Country == "US", 
                      !(`State/Province` %in% c("AK", "HI"))),
               aes(x = Longitude, y = Latitude),
             size = .3,
             alpha = .4,
             color = "gold1") +
  expand_limits(x = state_map$long, y = state_map$lat) + 
  scale_fill_gradient2() +
  labs(title = "Starbucks Most Popular on the West Coast",
       caption = "Viz by @lisalendway",
       fill = "Starbucks per 10000 People") +
  theme_map() +
  theme(legend.background = element_blank(), 
        plot.title = element_text(hjust = 0.5, face = "bold", size = 14))

The number of Starbucks per 10,000 people is higher on the West Coast, which the color is darker. The distribution is similar on the East coast, but the number of Starbucks per 10,000 people is much lower than the east coast. Central US has relatively lower distribution of Starbucks, as the result, the number of Starbucks per 10,000 people is also lower than the West Coast, but similar with the East Coast.

A few of your favorite things (leaflet)

8. In this exercise, you are going to create a single map of some of your favorite places! The end result will be one map that satisfies the criteria below.

• Create a data set using the tibble() function that has 10-15 rows of your favorite places. The columns will be the name of the location, the latitude, the longitude, and a column that indicates if it is in your top 3 favorite locations or not. For an example of how to use tibble(), look at the favorite_stp_by_lisa I created in the data R code chunk at the beginning.

• Create a leaflet map that uses circles to indicate your favorite places. Label them with the name of the place. Choose the base map you like best. Color your 3 favorite places differently than the ones that are not in your top 3 (HINT: colorFactor()). Add a legend that explains what the colors mean.

• Connect all your locations together with a line in a meaningful way (you may need to order them differently in the original data).

• If there are other variables you want to add that could enhance your plot, do that now.

favorite_place <- tibble(
  place = c("Home", "Forbiden City", "Hu Da", "Wangfujing", "CBD", "Tongheju", "Sanlitun", "Yonghegong", "Haidilao", "Chaoyang Dayuecheng"), 
  long = c(116.3799431, 116.3884302, 116.3621655, 116.4091913, 116.4583833, 116.3526585, 116.4510416, 116.4151015,116.3030231,116.5161446),
  lat = c(40.0085367, 39.9167136, 39.9167903, 39.9112741, 39.9102981, 39.9138851, 39.9358941, 39.9476753,39.921649,39.923893),
  top3 = c("yes", "yes", "yes",
           "no", "yes", "yes",
           "no", "yes", "yes", "no")
)

pal <- colorFactor(c("darkorchid4", "goldenrod1"),
                   domain = favorite_place$top3)

leaflet(data = favorite_place) %>% 
  addTiles() %>% 
  addPolylines(lng = ~long, 
               lat = ~lat, 
               label = ~place,
               weight = 2, 
               opacity = .5, 
               color = "darkred") %>% 
  addCircles(lng = ~long, 
             lat = ~lat, 
             label = ~place, 
             weight = 10, 
             opacity = 1, 
             color = ~pal(top3)) %>% 
  addLegend(position = "bottomright", 
            pal = pal,
            values = ~top3)

Revisiting old datasets

This section will revisit some datasets we have used previously and bring in a mapping component.

Bicycle-Use Patterns

The data come from Washington, DC and cover the last quarter of 2014.

Two data tables are available:

• Trips contains records of individual rentals
• Stations gives the locations of the bike rental stations

Here is the code to read in the data. We do this a little differently than usualy, which is why it is included here rather than at the top of this file. To avoid repeatedly re-reading the files, start the data import chunk with {r cache = TRUE} rather than the usual {r}. This code reads in the large dataset right away.

data_site <- 
  "https://www.macalester.edu/~dshuman1/data/112/2014-Q4-Trips-History-Data.rds" 
Trips <- readRDS(gzcon(url(data_site)))
Stations<-read_csv("http://www.macalester.edu/~dshuman1/data/112/DC-Stations.csv")

## Rows: 347 Columns: 5

## ─ Column specification ────────────────────────────
## Delimiter: ","
## chr (1): name
## dbl (4): lat, long, nbBikes, nbEmptyDocks

## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

9. Use the latitude and longitude variables in Stations to make a visualization of the total number of departures from each station in the Trips data. Use either color or size to show the variation in number of departures. This time, plot the points on top of a map. Use any of the mapping tools you’d like.

departure_station <- Trips %>% 
  left_join(Stations, by = c("sstation" = "name")) %>% 
  group_by(lat, long) %>% 
  summarize(n = n(), 
            prop_casual = mean(client == "Casual"))

## `summarise()` has grouped output by 'lat'. You can override using the `.groups` argument.

dc_map <- get_stamenmap(
  bbox = c(left = -77.2000, bottom = 38.7128, right = -76.5550, top = 39.0826),
  maptype = "toner-background",
  zoom = 11
)

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ggmap(dc_map) +
  geom_point(data = departure_station, 
             aes(x = long, y = lat, color = n),
             alpha = .8, shape = 18) +
  scale_color_viridis_c() +
  theme_map() +
  theme(legend.background = element_blank())

## Warning: Removed 21 rows containing missing values (geom_point).

10. Only 14.4% of the trips in our data are carried out by casual users. Create a plot that shows which area(s) have stations with a much higher percentage of departures by casual users. What patterns do you notice? Also plot this on top of a map. I think it will be more clear what the patterns are.

zoom_dc_map <- get_stamenmap(
  bbox = c(left = -77.1436, bottom = 38.6791, right = -76.9324, top = 39.0100),
  maptype = "terrain-background",
  zoom = 10
) 

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ggmap(zoom_dc_map) +
  geom_point(data = departure_station,
             aes(x = long, y = lat, color = prop_casual),
             size = 1.5,
             alpha = .8, shape = 18) +
  scale_color_viridis_c() +
  labs(title = "Proportion of Casual Client Riders by Station", 
       color = "") +
  theme_map() +
  theme(legend.background = element_blank(),
        legend.justification = "center",
        legend.position = "bottom",
        legend.direction = "horizontal",
        plot.title = element_text(hjust = 0.5, face = "bold", size = 14))

## Warning: Removed 23 rows containing missing values (geom_point).

The stations along the river has a higher proportion of casual client riders, and more stations are at the northern side of the river.

COVID-19 data

The following exercises will use the COVID-19 data from the NYT.

11. Create a map that colors the states by the most recent cumulative number of COVID-19 cases (remember, these data report cumulative numbers so you don’t need to compute that). Describe what you see. What is the problem with this map?

state_map <- map_data("state")
covid19 %>% 
  group_by(state) %>% 
  summarize(rec_cases = max(cases)) %>% 
  mutate(state = str_to_lower(state)) %>% 
  ggplot() +
  geom_map(map = state_map, 
           aes(map_id = state,
               fill = rec_cases)) +
  expand_limits(x = state_map$long, y = state_map$lat) +
  scale_fill_gradient2() +
  labs(title = "Number of COVID-19 Cases in Each States",
       fill = "Covid cases",
       caption = "Viz by @lisalendway") +
  theme_map() +
  theme(legend.background = element_blank(), 
        plot.title = element_text(hjust = 0.5, face = "bold", size = 14))

California has the most COVID cases and northern part of the US seems to have less cases then other states. The problem of this map is that each states have different population, which will affect the number of cases. In this graph, it didn’t consider the density of cases.

12. Now add the population of each state to the dataset and color the states by most recent cumulative cases/10,000 people. See the code for doing this with the Starbucks data. You will need to make some modifications.

state_map <- map_data("state")
covid19 %>% 
  group_by(state) %>% 
  summarize(rec_cases = max(cases)) %>% 
  mutate(state = str_to_lower(state)) %>% 
  left_join(census_pop_est_2018,
            by = c("state" = "state")) %>% 
  mutate(rec_cases_per_10000 = (rec_cases/est_pop_2018)*10000) %>% 
  ggplot() +
  geom_map(map = state_map, 
           aes(map_id = state,
               fill = rec_cases_per_10000)) +
  expand_limits(x = state_map$long, y = state_map$lat) +
  scale_fill_gradient2() +
  labs(title = "Number of COVID-19 Cases per 10,000 people in Each States",
       fill = "Covid cases",
       caption = "Viz by @lisalendway") +
  theme_map() +
  theme(legend.background = element_blank(), 
        plot.title = element_text(hjust = 0.5, face = "bold", size = 14))

13. CHALLENGE Choose 4 dates spread over the time period of the data and create the same map as in exercise 12 for each of the dates. Display the four graphs together using faceting. What do you notice?

Minneapolis police stops

These exercises use the datasets MplsStops and MplsDemo from the carData library. Search for them in Help to find out more information.

14. Use the MplsStops dataset to find out how many stops there were for each neighborhood and the proportion of stops that were for a suspicious vehicle or person. Sort the results from most to least number of stops. Save this as a dataset called mpls_suspicious and display the table.

mpls_suspicious <- MplsStops %>% 
  group_by(neighborhood) %>% 
  summarise(n = n(),
            prop = mean(problem == "suspicious")) %>% 
  arrange(desc(n))
mpls_suspicious

15. Use a leaflet map and the MplsStops dataset to display each of the stops on a map as a small point. Color the points differently depending on whether they were for suspicious vehicle/person or a traffic stop (the problem variable). HINTS: use addCircleMarkers, set stroke = FAlSE, use colorFactor() to create a palette.

pal <- colorFactor(c("darkgoldenrod4", "gold"), 
                        domain = c("suspicious", "traffic"))

leaflet(MplsStops) %>% 
  addProviderTiles(providers$CartoDB.Positron) %>% 
  addCircleMarkers(lng = ~long, 
                   lat = ~lat,
                   stroke = FALSE,
                   radius = 3,
                   opacity = 0.7, 
                   fillColor = ~pal(problem)) 

16. Save the folder from moodle called Minneapolis_Neighborhoods into your project/repository folder for this assignment. Make sure the folder is called Minneapolis_Neighborhoods. Use the code below to read in the data and make sure to delete the eval=FALSE. Although it looks like it only links to the .sph file, you need the entire folder of files to create the mpls_nbhd data set. These data contain information about the geometries of the Minneapolis neighborhoods. Using the mpls_nbhd dataset as the base file, join the mpls_suspicious and MplsDemo datasets to it by neighborhood (careful, they are named different things in the different files). Call this new dataset mpls_all.

mpls_nbhd <- st_read("Minneapolis_Neighborhoods/Minneapolis_Neighborhoods.shp", quiet = TRUE)

17. Use leaflet to create a map from the mpls_all data that colors the neighborhoods by prop_suspicious. Display the neighborhood name as you scroll over it. Describe what you observe in the map.

mpls_all <- mpls_nbhd %>% 
  left_join(MplsDemo, 
            by = c("BDNAME" = "neighborhood")) %>% 
   left_join(mpls_suspicious, 
            by = c("BDNAME" = "neighborhood"))

mpls_pal <- colorFactor("viridis", 
                        domain = mpls_all$prop)
leaflet(mpls_all) %>% 
  addProviderTiles(providers$Stamen.TonerLite) %>% 
  addPolygons(fillOpacity = .7,
              weight = 2,
              opacity = 1,
              color = "white",
              dashArray = "3",
              fillColor = ~mpls_pal(prop))  %>%
  addLegend(pal = mpls_pal, 
            opacity = .7,
            values = ~prop,
            title = NULL,
            position = "bottomright")

The light yellow area represents a higher proportion of stops was for suspicious vehicles. In this graph, it seems the south eastern part of Minneapolis has a higher proportion than other area, and north eastern seems to have a relatively lower proportion than other area.

18. Use leaflet to create a map of your own choosing. Come up with a question you want to try to answer and use the map to help answer that question. Describe what your map shows.

mpls_pov_pal <- colorFactor("viridis", 
                        domain = mpls_all$poverty,
                        reverse = TRUE)
leaflet(mpls_all) %>% 
  addProviderTiles(providers$CartoDB.Positron) %>%
  addPolygons(fillOpacity = .7,
              weight = 2,
              opacity = 1,
              color = "white",
              dashArray = "3",
              fillColor = ~mpls_pov_pal(poverty))  %>%
  addLegend(pal = mpls_pov_pal, 
            opacity = .7,
            values = ~poverty,
            title = NULL,
            position = "bottomright")

This diagram shows the proportion of poverty in each area in Minneapolis. I used the reverse color in this diagram, which shows the darker the area the higher the proportion of poverty. It seems the south eastern part of Minneapolis has relatively lower proportion of poverty than other area, and north western has a relatively higher proportion of poverty compares to other area.

GitHub link

19. Below, provide a link to your GitHub page with this set of Weekly Exercises. Specifically, if the name of the file is 04_exercises.Rmd, provide a link to the 04_exercises.md file, which is the one that will be most readable on GitHub.

https://github.com/MonicccccaL/Exercise4

https://github.com/MonicccccaL/Exercise4/blob/main/Exercise4.md

DID YOU REMEMBER TO UNCOMMENT THE OPTIONS AT THE TOP?